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压力对基于脑电图信号的个人识别系统的影响
Eman A Abdel-Ghaffar1, May Salama1
1Electrical Engineering Department, Faculty of Engineering Shoubra, Benha University, Cairo 11629, Egypt.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
压力对脑电图 (EEG) 识别系统产生重大影响. 在平静状态下注册和在压力状态下识别会降低表现,强调需要耐压力生物识别解决方案.
科学领域:
- 生物识别和人机交互的人机交互
- 神经科学和信号处理
背景情况:
- 电脑电图 (EEG) 信号提供了独特的生物识别能力.
- 对可靠的个人识别系统来说,EEG信号的稳定性至关重要.
- 人类的情绪状态,特别是压力,可以显著改变EEG信号特征.
研究的目的:
- 调查心理和情绪压力对基于EEG的个人识别系统性能的影响.
- 在不同压力条件下比较传统机器学习和深度学习方法的有效性.
主要方法:
- 进行了两项实验:一项使用手工制作的EEG特征 (时间,频率,非线性) 与机器学习分类器,另一项使用深度学习模型的原始EEG信号.
- 两种不同的数据集,SAM 40和DEAP,被用来检查各种类型的精神和情绪压力.
- 在不同的引发压力的场景下,基于识别准确度来评估性能.
主要成果:
- 在放松状态中注册,然后在压力状态中识别,会对系统性能产生负面影响.
- 对于DEAP数据集,准确率为99.67% (平静) 和96.67% (压力).
- 对于SAM 40数据集,准确度从99.67% (放松) 下降到91.67% (算术应力).
结论:
- 精神和情绪压力明显降低了基于EEG的识别系统的准确性.
- 这些发现强调了当前EEG生物识别系统对生理和心理变化的脆弱性.
- 未来的研究应该专注于开发抗压力电磁波电图识别方法.
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